Conformalized Strategy-Proof Auctions

2024-05-20

AI Summary

This paper addresses the lack of strategy-proofness (i.e., incentive compatibility) guarantees during the testing phase of machine learning–driven multi-item, multi-bidder auctions. We introduce a statistically grounded definition of strategy-proofness and pioneer the integration of conformal prediction into mechanism design. Methodologically, we unify conformal prediction, regret modeling, and differentiable economics to construct a regret-predictive auction acceptance rule—yielding a data-driven mechanism that is both verifiable and provably strategy-proof with high probability. Theoretically, we establish rigorous statistical strategy-proofness guarantees. Empirically, our mechanism maintains high revenue while strictly bounding the probability of high bidder regret below a prespecified confidence level—outperforming both zero-regret enforcement and naive ML-based baselines.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance

Abstract

Auctions are key for maximizing sellers' revenue and ensuring truthful bidding among buyers. Recently, an approach known as differentiable economics based on machine learning (ML) has shown promise in learning powerful auction mechanisms for multiple items and participants. However, this approach has no guarantee of strategy-proofness at test time. Strategy-proofness is crucial as it ensures that buyers are incentivized to bid their true valuations, leading to optimal and fair auction outcomes without the risk of manipulation. In this work, we propose a formulation of statistical strategy-proofness auction mechanism, ensuring that the probability of regret exceeding a predefined threshold is strictly controlled. Building upon conformal prediction techniques, we develop an auction acceptance rule that leverages regret predictions to guarantee that the data-driven auction mechanism meets the statistical strategy-proofness requirement with high probability. Our approach represents a practical middle-ground between two extremes: forcing zero-regret at the cost of significant revenue loss, and naively using ML to construct auctions with the hope of attaining low regret at test time. Numerical experiments demonstrate the necessity of the proposed method, the validity of our theoretical result, and its applicability.

Problem

• Ensuring strategy-proofness in auctions
• Controlling regret probability threshold
• Balancing revenue and ML-based auctions

Innovation

• Conformal prediction techniques
• Statistical strategy-proofness auction
• Regret predictions guarantee